See how autonomous robots can create shock-absorbing shapes that humans just can't achieve, and what that means for designing safer helmets, packaging, car bumpers, and more.
engineering
Watch an autonomous robot make a splash–A shape with absorbent properties that humans cannot match—What does this mean for designing safer helmets, packaging, car bumpers, and more?
In a Boston University engineering lab, a robotic arm drops small plastic objects into a box that sits flush against the floor to catch the drop. One by one, these tiny structures — feather-light cylinders less than an inch tall — are packed into the box. Some are red, some blue, purple, green, and black.
Each object is the result of an experiment in robotic autonomy, where the robot is learning to explore and create the most efficient energy-absorbing shape it has ever seen.
Watch as the robot completes a full experiment, from printing 3D shapes to crushing them under a metal plate and dropping discarded objects into a box.
The robot creates a small plastic structure on a 3D printer, records its shape and size, moves it onto a flat metal surface, then crushes it with the equivalent pressure of an adult Arabian horse standing on a quarter. The robot then measures how much energy the structure absorbed and how its shape changed after compression, recording all the details in a vast database. It then drops the crushed object into a box, wipes the metal plate clean, and prepares to print and test the next piece. It is only slightly different from its predecessor, and its design and dimensions are fine-tuned by the robot's computer algorithms based on all previous experiments. This is the basis of something called Bayesian optimization. With each experiment, the 3D structure becomes better and better at absorbing the impact of being crushed.
These experiments were made possible thanks to the work of Keith Brown, an associate professor of mechanical engineering at ENG, and his team at the KABlab. Named MAMA BEAR (short for its longer official name, Mechanics of Additively Manufactured Architectures Bayesian Experimental Autonomous Researcher), the robot has evolved since it was first conceptualized by Brown and his lab in 2018. By 2021, the lab had tasked the machine with a quest to create a shape that absorbs energy, a property known as mechanical energy absorption efficiency. This current iteration has been running continuously for more than three years, with more than 25,000 3D printed structures packed into dozens of boxes.
Why so many shapes? The ability to absorb energy efficiently has myriad applications, from cushioning sensitive electronic devices shipped around the world to knee pads and wrist guards for athletes. “From this data library, we can build better car bumpers or better packaging equipment, for example,” Brown says.
To function ideally, a structure must be perfectly balanced: It can't be so strong that it damages what it's supposed to protect, but it needs to be strong enough to absorb impacts. The best structures observed before Mama Bear had an energy absorption efficiency of about 71 percent, Brown says. But on a chilly afternoon in January 2023, Brown's lab watched as the robot achieved 75 percent efficiency, shattering the known record. The results: Nature Communications.
“When we started the project, we weren't sure we'd get this record-breaking shape,” says Kelsey Snapp (ENG'25), a doctoral student in the Brown lab who oversees MAMA BEAR. “Slowly but surely, we kept making progress, bit by bit, and we made a breakthrough.”
evolution
Click the play icon to move each shape and take a closer look at the MAMA BEAR research. Scroll to the end using the yellow arrows to see the record-breaking shapes and watch the evolution of the structure. (Note: these are digital renderings of the real thing. For a more precise technical description, see Nature Communications paper.)
The record-breaking structure is quite different from what researchers expected: It has four points, is shaped like thin petals, and is taller and narrower than earlier designs.
“We have a ton of mechanical data here and we're excited to use it to learn lessons about design in general,” Brown says.
Their vast data is already being used in the field for the first time to help design new helmet padding for U.S. Army soldiers. Brown, SNAP, and project collaborator Emily Whiting, an associate professor of computer science at Boston University's School of Humanities and Sciences, worked with the U.S. Army recently to conduct field tests to ensure helmets using their patent-pending padding were comfortable and adequately protective in impacts. The 3D structure used in the padding differs from the record-breaking section in that it is softer in the middle and has a lower height for increased comfort.
Use the slider to toggle between a 3D digital rendering and the interior frame of Structure #07214, which is 56 percent efficient.
MAMA BEAR isn't Brown's only autonomous research robot. His lab has other “BEAR” robots that perform a variety of tasks. For example, Nano BEAR uses a technique called atomic force microscopy to study materials behavior at the molecular level. Brown is also working with ENG assistant professor of mechanical engineering Jörg Werner to develop another system, PANDA (short for Polymer Analysis and Discovery Array) BEAR, to test thousands of thin polymer materials to find the best ones for batteries.
“These are all robots doing research,” Brown says. “The philosophy is that by using a combination of machine learning and automation, we can greatly increase the speed of research.”
“It's not just fast,” Snap adds. “It can do things that you can't normally do. It can reach structures and goals that you couldn't reach any other way because it would be too expensive or too time consuming.” Snap has worked closely with Mama Bear since the experiment began in 2021, giving the robot machine vision and the ability to wash its own test plates.
KABlab wants to further demonstrate the importance of autonomous research. Brown wants to continue collaborating with scientists from different disciplines who need to test huge numbers of structures and solutions. Even though they've already broken a record, “we don't know if we've reached maximum efficiency,” Brown says. That means it could break the record again. So while MAMA BEAR will keep running and pushing its limits further, Brown and his team will consider what other uses the database could be useful for. They're also exploring how the more than 25,000 pieces can be unwound and reloaded into the 3D printer, so the material can be reused for further experiments.
“We will continue to study this system because, like many other material properties, mechanical efficiency can only be accurately measured through experimentation,” Brown says, “and using the self-driving lab will enable us to select the best experiments and run them as quickly as possible.”
The research was supported by the National Science Foundation and the U.S. Army.
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